#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import sys
import unittest

import numpy as np

sys.path.append("..")

from op_test_xpu import XPUOpTest
from xpu.get_test_cover_info import (
    XPUOpTestWrapper,
    create_test_class,
    get_xpu_op_support_types,
)

import paddle
import paddle.nn.functional as F

paddle.enable_static()
np.random.seed(10)


def temporal_shift(x, seg_num, shift_ratio, data_format):
    if data_format == "NHWC":
        x = np.transpose(x, (0, 3, 1, 2))
    shape = x.shape
    reshape_x = x.reshape((-1, seg_num, shape[1], shape[2], shape[3]))
    pad_x = np.pad(
        reshape_x, ((0, 0), (1, 1), (0, 0), (0, 0), (0, 0)), 'constant'
    )
    c1 = int(shape[1] * shift_ratio)
    c2 = int(shape[1] * 2 * shift_ratio)
    slice1 = pad_x[:, :seg_num, :c1, :, :]
    slice2 = pad_x[:, 2 : seg_num + 2, c1:c2, :, :]
    slice3 = pad_x[:, 1 : seg_num + 1, c2:, :, :]
    concat_x = np.concatenate([slice1, slice2, slice3], axis=2)
    out = concat_x.reshape(shape)
    if data_format == "NHWC":
        out = np.transpose(out, (0, 2, 3, 1))
    return out


class XPUTestTemporalShiftOp(XPUOpTestWrapper):
    def __init__(self):
        self.op_name = "temporal_shift"
        self.use_dynamic_create_class = False

    class TestXPUTemporalShift(XPUOpTest):
        def setUp(self):
            self.initTestCase()
            self.op_type = 'temporal_shift'
            self.python_api = F.temporal_shift
            self.use_xpu = True
            x = np.random.random(self.x_shape).astype(self.dtype)

            self.attrs = {
                "seg_num": self.seg_num,
                "shift_ratio": self.shift_ratio,
                "data_format": self.data_format,
            }

            self.inputs = {
                "X": x,
            }

            output = temporal_shift(
                x, self.seg_num, self.shift_ratio, self.data_format
            )
            self.outputs = {"Out": output}
            self.python_out_sig = ["Out"]

        def test_check_output(self):
            self.check_output(check_eager=True)

        def test_check_grad(self):
            self.check_grad(['X'], 'Out', check_eager=True)

        def initTestCase(self):
            self.x_shape = (6, 4, 4, 4)
            self.seg_num = 3
            self.shift_ratio = 0.25
            self.dtype = 'float32'
            self.data_format = 'NCHW'

    class TestXPUTemporalShift2(TestXPUTemporalShift):
        def initTestCase(self):
            self.x_shape = (1, 1, 1, 1)
            self.seg_num = 1
            self.shift_ratio = 0.1
            self.dtype = 'float32'
            self.data_format = 'NCHW'

    class TestXPUTemporalShift3(TestXPUTemporalShift):
        def initTestCase(self):
            self.x_shape = (4, 9, 1, 1)
            self.seg_num = 2
            self.shift_ratio = 0.2
            self.dtype = 'float32'
            self.data_format = 'NCHW'

    class TestXPUTemporalShift4(TestXPUTemporalShift):
        def initTestCase(self):
            self.x_shape = (4, 1, 10, 10)
            self.seg_num = 2
            self.shift_ratio = 0.3
            self.dtype = 'float32'
            self.data_format = 'NCHW'

    class TestXPUTemporalShift5(TestXPUTemporalShift):
        def initTestCase(self):
            self.x_shape = (1, 1, 1, 1)
            self.seg_num = 1
            self.shift_ratio = 0.3
            self.dtype = 'float32'
            self.data_format = 'NHWC'

    class TestXPUTemporalShift6(TestXPUTemporalShift):
        def initTestCase(self):
            self.x_shape = (6, 5, 5, 1)
            self.seg_num = 3
            self.shift_ratio = 0.25
            self.dtype = 'float32'
            self.data_format = 'NHWC'

    class TestXPUTemporalShift7(TestXPUTemporalShift):
        def initTestCase(self):
            self.x_shape = (9, 1, 1, 4)
            self.seg_num = 3
            self.shift_ratio = 0.45
            self.dtype = 'float32'
            self.data_format = 'NHWC'


support_types = get_xpu_op_support_types('temporal_shift')
for stype in support_types:
    create_test_class(globals(), XPUTestTemporalShiftOp, stype)

if __name__ == "__main__":
    paddle.enable_static()
    unittest.main()
